Executives don't need to see every AI metric. They need the right metrics—the ones that signal whether AI is creating value, under control, and aligned with strategy. This guide shows you how to design an AI executive dashboard that provides visibility without overload.
Executive Summary
- Less is more — A good dashboard has 10-15 metrics, not 50; focus on what drives decisions
- Four categories cover the landscape — Value, Adoption, Risk, and Portfolio health
- Traffic lights work — Red/amber/green indicators enable quick scanning
- Trends beat snapshots — Show direction, not just current state
- Drill-down available — Summary view for executives, detail accessible on demand
- Regular cadence — Monthly updates, quarterly deep dives
- Action-oriented — Every red indicator should trigger a defined response
Why This Matters Now
Accountability Gap. AI investments are growing, but visibility into performance often lags. Executives can't hold teams accountable for what they can't see. (/insights/ai-ceo-guide-executive)
Board Expectations. Directors increasingly ask about AI. A good dashboard provides answers before they ask. (/insights/ai-board-reporting-template-updates)
Course Correction. Problems detected early are cheaper to fix. Dashboards enable early intervention.
Strategic Alignment. When AI metrics are visible, they get attention. What gets measured gets managed.
The Four Dashboard Quadrants
Quadrant 1: Value
| Metric | Definition | Target Example |
|---|---|---|
| AI-Influenced Revenue | Revenue from AI-enhanced products | Growing >10% QoQ |
| Cost Reduction | Documented savings from automation | Meeting business case |
| Customer Satisfaction | CSAT for AI touchpoints | ≥ human baseline |
| Time Saved | Hours saved through AI | On track vs. projection |
Quadrant 2: Adoption
| Metric | Definition | Target Example |
|---|---|---|
| AI User Count | Employees using AI tools | Growing monthly |
| Tool Utilization | Usage frequency | >60% licensed users |
| Training Completion | Employees trained | >80% |
| Department Coverage | Departments with AI | Growing |
Quadrant 3: Risk
| Metric | Definition | Target Example |
|---|---|---|
| AI Incidents | Count and severity | Trending down |
| Open AI Risks | Unmitigated risks | Stable/declining |
| Policy Compliance | Compliance rate | >95% |
| Audit Findings | Open findings | Zero high-severity |
(/insights/ai-monitoring-metrics-kpis) (/insights/ai-risk-register-template)
Quadrant 4: Portfolio
| Metric | Definition | Target Example |
|---|---|---|
| Active AI Projects | Count by stage | Healthy pipeline |
| AI Investment | Spend vs. budget | On budget |
| ROI Realization | Actual vs. projected | ≥80% of projection |
| Time to Value | Start to production | Improving |
Dashboard Template
═══════════════════════════════════════════════════════════
AI EXECUTIVE DASHBOARD — [Month Year]
═══════════════════════════════════════════════════════════
OVERALL STATUS: 🟢 Green / 🟡 Amber / 🔴 Red
───────────────────────────────────────────────────────────
VALUE STATUS: 🟢
───────────────────────────────────────────────────────────
AI-Influenced Revenue: $2.3M (+15% QoQ) 🟢
Cost Reduction: $450K YTD 🟢
Customer Satisfaction: 4.2/5.0 🟢
Productivity Gain: +8% vs. baseline 🟡
───────────────────────────────────────────────────────────
ADOPTION STATUS: 🟡
───────────────────────────────────────────────────────────
Active AI Users: 342 (+23 this month) 🟢
Tool Utilization: 58% 🟡
AI Projects in Prod: 7 🟢
Training Completion: 72% 🟡
───────────────────────────────────────────────────────────
RISK STATUS: 🟢
───────────────────────────────────────────────────────────
Incidents (Month): 1 (Low severity) 🟢
Open High Risks: 2 🟢
Policy Compliance: 96% 🟢
Audit Findings: 0 open 🟢
───────────────────────────────────────────────────────────
PORTFOLIO STATUS: 🟢
───────────────────────────────────────────────────────────
Pipeline: 3 pilot | 7 prod | 2 scale
Spend vs. Budget: 92% ($1.8M of $2M) 🟢
ROI Realization: 85% of projected 🟢
───────────────────────────────────────────────────────────
KEY HIGHLIGHTS & ACTIONS REQUIRED
───────────────────────────────────────────────────────────
+ Customer service chatbot exceeded 40% deflection target
- Training completion behind target; remediation in progress
□ Approve Q4 AI investment proposal (Board)
Design Principles
1. One-Page Summary — The main dashboard fits on one page. Detail in appendices.
2. Traffic Light Status — 🟢 On track / 🟡 Watch / 🔴 Requires attention
3. Trends Over Snapshots — Show 3-6 months direction, not just current state.
4. Context Matters — Compare to targets, prior periods, or benchmarks.
5. Drill-Down Available — Summary is entry point; detail accessible on demand.
Common Failure Modes
Too Many Metrics. Limit to 10-15. If everything is measured, nothing is prioritized.
Vanity Metrics. Metrics that look good but don't drive decisions.
Stale Data. Dashboards with outdated information lose credibility.
No Thresholds. Without defined targets, everything is green.
No Actions. Dashboard reports problems but triggers no response.
Checklist for AI Executive Dashboards
- Limited to 10-15 key metrics
- Four quadrants covered (Value, Adoption, Risk, Portfolio)
- Traffic light indicators with defined thresholds
- Trend data included
- Fits on one page
- Drill-down detail available
- Data sources reliable
- Update cadence established
- Actions linked to red indicators
Frequently Asked Questions
Ready to Build AI Visibility?
Good governance requires good visibility. An effective AI dashboard enables oversight without micromanagement.
Book an AI Readiness Audit to assess your AI governance and get help designing metrics that matter.
[Contact Pertama Partners →]
References
- Gartner. (2024). "Effective Technology Dashboards for Executives."
- MIT Sloan Management Review. (2024). "AI Metrics That Matter."
- McKinsey & Company. (2024). "Measuring AI Value."
- Deloitte. (2024). "AI Portfolio Management Metrics."
Frequently Asked Questions
Monthly is typical for executive dashboards. Some metrics may be tracked more frequently; others quarterly.
References
- Effective Technology Dashboards for Executives.. Gartner (2024)
- AI Metrics That Matter.. MIT Sloan Management Review (2024)
- Measuring AI Value.. McKinsey & Company (2024)
- AI Portfolio Management Metrics.. Deloitte (2024)
